This invention relates to the field of digital rock physics and, more particularly, to methods to estimate geophysical properties of rocks which can include those with very fine pore structures such as shales.
The present invention relates to a method and system to analyze a porous medium with very fine pores, such as a shale reservoir rock sample, to obtain values of porosity, permeability, total organic content, and other geophysical data. Shale is an unconventional source of oil and/or gas. Shale rocks have not been studied extensively due to the fact that they traditionally were thought of as the source rock and not a potential reservoir because of their low porosity and permeability values. However, there are new methods to extract the oil and gas within these rocks, and therefore, there is great interest in analysis methods to characterize these rocks to better understand the mechanics of production from shales.
Traditionally, there were only limited ways to analyze shale samples, and this began with scanning electron microscopes (SEM). The SEM image provides a two-dimensional (2D) picture or image of the sample that typically has a resolution of approximately 15-100 nanometers. Using only two-dimensional images, however, one is only able to estimate porosity and organic content, and one can only approximate permeability because permeability is a three dimensional property.
In scanning electron microscopy (SEM), multitudes of electrons are generated as the result of the energetic bombardment of a primary electron beam (PE) directed on the sample, such as illustrated in FIG. 1. These generated electrons can carry distinct structural information of the sample, and can differ from one another in origin, energy and traveling direction. For example, type-I secondary electrons (SE-1) emit with a high angle at a close proximity from the impact point of the primary electron beam (PE), and carry high-resolution, surface sensitive information of the sample. Type-II secondary electrons (SE-2) are generated from a deeper and wider volume of the sample surface than the type-I secondary electrons (SE-1s), and emit from the sample surface with a lower angle trajectory, and carry intrinsically lower resolution, topographical information. Similarly, singly scattered backscattered electrons (BSE-1) tend to emit at a high angle and are closely linked to compositional contrast, while multiply-scattered BSEs (BSE-2) emit from the sample surface at a lower angle trajectory, and their yields represent a mixture of composition and crystalline structures of the sample. Different detectors associated with the SEM system can be used to collect these different electrons. The detected electrons can be processed to generate an image of the scanned region of the sample. These and other details about SEM imaging are described, for example, in Huang, J., “Simultaneous Multi-channel Data Acquisition in Three Dimensions”, http://www.zeiss.de/C1256E4600307C70/EmbedTitelIntern/AN_Simultaneous_Multichannel_Data_Acquisition_in_Three_Dimensions_neu/$File/AN_Simultaneous_Multichannel_Data_Acquisition_in_Three_Dimensions.pdf, Carl Zeiss, 2011.
Along with technological advances and use of medical equipment to help analyze geologic features, advances in workflow have been created. For example, a workflow has been shown which has three basic steps of (a) 3D CT imaging and/or FIB-SEM (focused ion beam combined with SEM) imaging; (b) segmentation of the digital volume to quantitatively identify the components, including the mineral phases, organic-filled pores, and free-gas inclusions; and (c) computations of TOC (Total Organic Content), porosity, pore connectivity, and permeability in the three axis. Sisk et al, SPE 134582, “3D Visualization and Classification of Pore Structure and Pore Filling in Gas Shales”, 2010. Using FIB-SEM technology, a sample is analyzed in three dimensions by creating a plurality of two-dimensional images. The segmentation process can be done by, assigning gray scale ranges to features, and volumes can be constructed which show three dimensional distributions of these features. Curtis et al, SPE 137693, “Structural Characterization of Gas Shales on the Micro- and nano-Scales”, 2010. The features that are present within the rock can include, but are not limited to, pores, organic matter, and rock matrix. The process of analyzing shale rocks in three dimensions has greatly aided understanding of how a complex shale reservoir functions.
To enhance image quality, new preparation techniques have been proposed, such as those shown by Milner et al, SPE 138975, “Imaging Texture and Porosity in Mudstones and Shales: Comparison of Secondary and Ion-Milled Backscatter SEM methods”, 2010. Milner et al. shows two preparation techniques which were used for SEM imaging, wherein secondary electron images represent fresh, minimally gold-coated surfaces broken normal to bedding, and backscatter images show milled surfaces approximately 1.0 mm by 0.5 mm, created using a JEOL IB-9010 Argon-ion polisher. Both secondary and milled backscatter samples were imaged using a FEI NovaNano 630 field emission scanning electron microscope. An Argon ion polisher is used to polish the sample, so the image appears cleaner, and therefore is easier to segment. Following the polishing step, the sample is coated with gold, and then processed and imaged with an FIB-SEM. Within the SEM column there are two detectors, the ESB (BSE-1) and the SE-2. The two detectors help eliminate the shortfalls of only using one detector. When using only one detector, only one image is created, and it is difficult to discriminate between porosity, organic materials such as kerogen, and minerals. By using two detectors the SEM produces two different views of the sample, making it possible to quantify solid materials and pore space.
Previously, the assignee's inventors developed an improved image segmentation process, which is described in U.S. Patent Application No. 61/547,090, which is incorporated herein in its entirety by reference. This technique involves simultaneously creating two SEM images of the surface of a porous medium. One image is produced by detecting primary backscattered electrons (ESB) and a second image is produced by detecting secondary electrons (SE-2). These two images have different qualities in that the SE-2 produced images provide clear distinction of pore walls but organic content contained within the pores such as kerogen, distort the image resulting in estimates of porosity that are too low. The BSE produced image provides a different perspective on contained organic content and by aligning and analyzing these two images, a more accurate picture of pore space and organic content results.
In addition to these procedures, X-ray Diffraction (XRD) is commonly used to determine chemical composition and crystallographic structure of shales. XRD yields the atomic structure of materials and is based on the elastic scattering of X-rays from the electron clouds of the individual atoms in the system. As also described by the indicated Milner et al. document (SPE 138975), XRD data were generated from powdered samples analyzed with a Figaku Ultima III X-ray diffractometer, and results were interpreted using JADE software.
Large samples of porous rock are required in order to obtain estimates of rock properties such as permeability, porosity, elasticity and other properties that are typical of an entire subterranean rock formation or facies. One common sample used to estimate rock properties is a well core. Well cores are very small compared to an entire formation, so multiple well cores are typically taken, analyzed, and rock properties are interpolated in between geographic locations of the cores. When rock properties are estimated using digital rock physics, the problem of sample size versus formation or facies size is even greater. Digital rock physics techniques for estimating rock properties have the advantage that they can accurately produce digital images of very fine pore structures and identify small volumes of organic materials present in the pore structure of the rock. However, it is very time consuming and expensive to digitally scan very large samples to estimate rock properties. The difference in scale between the sample (core) and the pores contained in the sample can complicate pore analysis thereof. Scanning the entire sample at a resolution high enough to identify all of the pores can result in a complete assessment of the pore structure of the sample. However, scanning the entire sample at a resolution high enough to identify all of the pores is not practical due to the time and expense required to do a complete scan.
The present investigators have recognized that FIB-SEM systems work on relatively small sample size and as such, an efficient method is needed to initially overview or scout for areas of interest in analyzing a shale reservoir and then move to higher and higher magnifications (resolutions) on identified areas of interest. The present investigators further recognized that manual selection of rock samples for high resolution analysis can provide the geologist or reservoir engineer information about facies that are likely to contain organic matter, but the manual methods can be highly subjective, inconsistent, and expensive. The present investigators further have recognized that there is a need for a method of high resolution analysis of rock samples which incorporates reliable automated sample screening and analysis features.